The marine controlled source electromagnetic (“CSEM”) method typically uses a towed bipole source and deployed ocean-bottom receivers for mapping sub-seafloor resistivity variations; see, for example, U.S. Pat. No. 6,628,119 to Eidesmo et al.
The standard approaches for determining the survey receiver positions and source tow-line locations suffer from several limitations:                Available displays of areal subsurface coverage are typically inadequate to evaluate a given survey design;        There are so many variables that it can be difficult to tune a survey for specific geophysical objectives (e.g., types of data such as various components of E or B field, survey geometry parameters, reservoir and earth parameters, and inversion parameters);        Methods that rely on multiple inversions are very time consuming; and        Forward modeling approaches fail to examine the inversion null space issues (i.e., other models may give a similar data response).        
Maurer et al. (“Design strategies for electromagnetic geophysical surveys”, Inverse Problems 16, 1097-1117 (2000)) summarize the four families of current approaches for EM survey design. The first and most common approach involves using repeated forward modeling to look at the data that would result for various acquisition and earth scenarios. Most commonly, a simple sail-over source line is examined for the reservoir and no-reservoir cases to see if a reservoir would be detectable. This approach becomes intractable for reconnaissance survey optimization because of the many parameters to examine and the required number of forward models. It also does not examine null-space issues—i.e., that several models may have similar data so that the data analyst cannot readily distinguish among them.
The term null space means the collection of possible differences from a specified earth conductivity model that would produce little or no change in the specified acquired data. Strictly speaking, the null space would include only model differences that produce no change in the data, however, the term is used somewhat loosely herein to include model changes that produce only small data changes that would be less than the expected noise levels in the data. A null-space problem exists when two or more different conductivity structures produce very similar data and there is a significant exploration need to distinguish these cases. If these cases cannot be distinguished based on the given data, then an approach such as inversion will also be unable to distinguish them (unless additional geological or other a priori information is included or additional appropriate field data are acquired).
A second family of survey-design approaches examines data sensitivity with respect to model perturbations. A sensitivity display would illustrate the zones in the subsurface that most affect a particular data value for particular source and receiver locations. This display gives some idea of the areal extent of the zone that affects a particular measurement. Limitations of this approach include the need to model many parameters and measurement points and also the fact that the perturbation is relative to a particular starting model.
A third family of approaches examines a modified sensitivity plot—the “data importance” function. These functions express the influence of each data point on the final inversion result. This can be helpful in selecting the particular data that are most necessary in the survey design. A weakness is that the data importance is biased toward the most resolved portions of the model. Also the importance is necessarily determined with respect to a particular model example.
The fourth family of suggested approaches is based on global optimization. An objective function is minimized with respect to some simplified survey parameters. Because of the complexity of this approach, only simple cases are possible. An example might be to select the best 30 data points out of a set of 200 possible offset-frequency pairs. This approach is too limited for optimizing CSEM survey parameters because only small subsets of the model and data spaces can be considered.
In addition to these survey design approaches, Houck and Pavlov (“Evaluating reconnaissance CSEM survey designs using detection theory,” The Leading Edge 25, 994-1004 (2006)) present a Monte Carlo method that predicts the value of information for competing survey design scenarios. The main focus of this approach is to determine the probabilities of discriminating economic from sub-economic reservoirs of unknown position given a survey configuration and information about its reservoir detection capability.
Except in simple cases, the current techniques are not adequate for determining an optimal CSEM survey design. Some weaknesses in these approaches include an inability to deal with the numerous possible survey and geological parameters, a lack of adequate diagnostic displays, and the lack of a means to assess different models that would produce similar data (i.e., the null-space problem). There is a need for an improved technique that addresses these deficiencies, and the present invention satisfies this need.